{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T15:54:58Z","timestamp":1783180498766,"version":"3.54.6"},"reference-count":72,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T00:00:00Z","timestamp":1692316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>In recent years, limited works on EOG (electrooculography)-based biometric authentication systems have been carried out with eye movements or eye blinking activities in the current literature. EOGs have permanent and unique traits that can separate one individual from another. In this work, we have investigated FSST (Fourier Synchrosqueezing Transform)-ICA (Independent Component Analysis)-EMD (Empirical Mode Decomposition) robust framework-based EOG-biometric authentication (one-versus-others verification) performances using ensembled RNN (Recurrent Neural Network) deep models voluntary eye blinkings movements. FSST is implemented to provide accurate and dense temporal-spatial properties of EOGs on the state-of-the-art time-frequency matrix. ICA is a powerful statistical tool to decompose multiple recording electrodes. Finally, EMD is deployed to isolate EOG signals from the EEGs collected from the scalp. As our best knowledge, this is the first research attempt to explore the success of the FSST-ICA-EMD framework on EOG-biometric authentication generated via voluntary eye blinking activities in the limited EOG-related biometric literature. According to the promising results, improved and high recognition accuracies (ACC\/Accuracy: \u226599.99% and AUC\/Area under the Curve: 0.99) have been achieved in addition to the high TAR (true acceptance rate) scores (\u226598%) and low FAR (false acceptance rate) scores (\u22643.33%) in seven individuals. On the other hand, authentication and monitoring for online users\/students are becoming essential and important tasks due to the increase of the digital world (e-learning, e-banking, or e-government systems) and the COVID-19 pandemic. Especially in order to ensure reliable access, a highly scalable and affordable approach for authenticating the examinee without cheating or monitoring high-data-size video streaming is required in e-learning platforms and online education strategies. Hence, this work may present an approach that offers a sustainable, continuous, and reliable EOG-biometric authentication of digital applications, including e-learning platforms for users\/students.<\/jats:p>","DOI":"10.3390\/biomimetics8040378","type":"journal-article","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T10:28:48Z","timestamp":1692354528000},"page":"378","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Fourier Synchrosqueezing Transform-ICA-EMD Framework Based EOG-Biometric Sustainable and Continuous Authentication via Voluntary Eye Blinking Activities"],"prefix":"10.3390","volume":"8","author":[{"given":"Kutlucan","family":"Gorur","sequence":"first","affiliation":[{"name":"Electrical and Electronics Engineering Department, Band\u0131rma Onyedi Eylul University, 10250 Bal\u0131kesir, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113114","DOI":"10.1016\/j.eswa.2019.113114","article-title":"A Comprehensive Survey on the Biometric Recognition Systems Based on Physiological and Behavioral Modalities","volume":"143","author":"Dargan","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/TCE.2021.3055419","article-title":"A Robust Biometric Authentication System for Handheld Electronic Devices by Intelligently Combining 3D Finger Motions and Cerebral Responses","volume":"67","author":"Behera","year":"2021","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"14917","DOI":"10.1038\/s41598-020-72051-1","article-title":"Towards a Minimal EEG Channel Array for a Biometric System Using Resting-State and a Genetic Algorithm for Channel Selection","volume":"10","author":"Moctezuma","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jayarathne, I., Cohen, M., and Amarakeerthi, S. (2020). Person Identification from EEG Using Various Machine Learning Techniques with Inter-Hemispheric Amplitude Ratio. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0238872"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1007\/s00521-022-07795-0","article-title":"Biometric Identification System Using EEG Signals","volume":"35","author":"Tatar","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2635","DOI":"10.1109\/TIFS.2016.2577551","article-title":"A High-Security EEG-Based Login System with RSVP Stimuli and Dry Electrodes","volume":"11","author":"Chen","year":"2016","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5850","DOI":"10.1038\/s41598-020-62712-6","article-title":"Multi-Objective Optimization for EEG Channel Selection and Accurate Intruder Detection in an EEG-Based Subject Identification System","volume":"10","author":"Moctezuma","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LSENS.2020.3012653","article-title":"An LSTM-Based Model for Person Identification Using ECG Signal","volume":"4","author":"Jyotishi","year":"2020","journal-title":"IEEE Sensors Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ortega-Rodr\u00edguez, J., G\u00f3mez-Gonz\u00e1lez, J.F., and Pereda, E. (2023). Selection of the Minimum Number of EEG Sensors to Guarantee Biometric Identification of Individuals. Sensors, 23.","DOI":"10.3390\/s23094239"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1109\/TBCAS.2019.2892297","article-title":"CorNET: Deep Learning Framework for PPG-Based Heart Rate Estimation and Biometric Identification in Ambulant Environment","volume":"13","author":"Biswas","year":"2019","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/j.bbe.2018.06.004","article-title":"Glossokinetic Potential Based Tongue\u2013Machine Interface for 1-D Extraction Using Neural Networks","volume":"38","author":"Gorur","year":"2018","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.patrec.2015.07.034","article-title":"A New Multi-Level Approach to EEG Based Human Authentication Using Eye Blinking","volume":"82","author":"Ahmed","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_13","first-page":"903","article-title":"Use of Spontaneous Blinking for Application in Human Authentication","volume":"23","author":"Jalilifard","year":"2020","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1109\/LSP.2014.2374338","article-title":"A Novel Biometric Approach for Human Identification and Verification Using Eye Blinking Signal","volume":"22","author":"Ahmed","year":"2015","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.compbiomed.2012.10.005","article-title":"Biometric Verification of a Subject through Eye Movements","volume":"43","author":"Juhola","year":"2013","journal-title":"Comput. Biol. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1109\/JAS.2020.1003483","article-title":"NeuroBiometric: An Eye Blink Based Biometric Authentication System Using an Event-Based Neuromorphic Vision Sensor","volume":"8","author":"Chen","year":"2021","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"72398","DOI":"10.1109\/ACCESS.2021.3079375","article-title":"Online Student Authentication and Proctoring System Based on Multimodal Biometrics Technology","volume":"9","author":"Labayen","year":"2021","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"123349","DOI":"10.1109\/ACCESS.2022.3224034","article-title":"COVID-19 and E-Learning: An Exploratory Analysis of Research Topics and Interests in E-Learning during the Pandemic","volume":"10","author":"Gurcan","year":"2022","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gurcan, F., Boztas, G.D., Dalveren, G.G.M., and Derawi, M. (2023). Digital Transformation Strategies, Practices, and Trends: A Large-Scale Retrospective Study Based on Machine Learning. Sustainability, 15.","DOI":"10.3390\/su15097496"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ubah, A.E., Onakpojeruo, E.P., Ajamu, J., Mangai, T.R., Isa, A.M., Ayansina, N.B., and Al-Turjman, F. (2022, January 26\u201328). Biometrics Authentication Techniques in E-Learning Assessment. Proceedings of the 2022 International Conference on Artificial Intelligence of Things and Crowdsensing (AIoTCs), Nicosia, Cyprus.","DOI":"10.1109\/AIoTCs58181.2022.00105"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/TBIOM.2021.3112540","article-title":"TypeNet: Deep Learning Keystroke Biometrics","volume":"4","author":"Acien","year":"2022","journal-title":"IEEE Trans. Biom. Behav. Identity Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Haque, M.A., Haque, S., Zeba, S., Kumar, K., Ahmad, S., Rahman, M., Marisennayya, S., and Ahmed, L. (2023). Sustainable and Efficient E-Learning Internet of Things System through Blockchain Technology. E-Learn. Digit. Media.","DOI":"10.1177\/20427530231156711"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1007\/s12528-022-09327-0","article-title":"Exploring the Acceptance for E-Learning among Higher Education Students in India: Combining Technology Acceptance Model with External Variables","volume":"34","author":"Chahal","year":"2022","journal-title":"J. Comput. High. Educ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.patrec.2017.03.027","article-title":"A Multi-Biometric System for Continuous Student Authentication in e-Learning Platforms","volume":"113","author":"Fenu","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"100480","DOI":"10.1016\/j.measen.2022.100480","article-title":"IoT Enabled E-Learning System for Higher Education","volume":"24","author":"Kumar","year":"2022","journal-title":"Meas. Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"69573","DOI":"10.1109\/ACCESS.2021.3077663","article-title":"Feature Evaluation of Emerging E-Learning Systems Using Machine Learning: An Extensive Survey","volume":"9","author":"Aslam","year":"2021","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Rukhiran, M., Wong-In, S., and Netinant, P. (2023). User Acceptance Factors Related to Biometric Recognition Technologies of Examination Attendance in Higher Education: TAM Model. Sustainability, 15.","DOI":"10.3390\/su15043092"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sun, Y., and Nembhard, D.A. (2022). Static vs. Dynamic Representations and the Mediating Role of Behavioral Affect on E-Learning Outcomes. Int. J. Hum. Comput. Interact., 1\u201312.","DOI":"10.1080\/10447318.2022.2096187"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bogicevic Sretenovic, M., Milenkovic, I., Jovanovic, B., Simic, D., Minovic, M., and Milovanovic, M. (2020). Bringing Biometric Sensors to the Classroom: A Fingerprint Acquisition Laboratory for Improving Student Motivation and Commitment. Appl. Sci., 10.","DOI":"10.3390\/app10030880"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3078","DOI":"10.1109\/JSEN.2019.2956072","article-title":"Time-Frequency Domain Deep Convolutional Neural Network for the Classification of Focal and Non-Focal EEG Signals","volume":"20","author":"Madhavan","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.bspc.2019.04.023","article-title":"Synchrosqueezing Transform Based Feature Extraction from EEG Signals for Emotional State Prediction","volume":"52","author":"Ozel","year":"2019","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.dsp.2018.07.003","article-title":"Emotion Recognition Based on Time\u2013Frequency Distribution of EEG Signals Using Multivariate Synchrosqueezing Transform","volume":"81","author":"Mert","year":"2018","journal-title":"Digit. Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2641","DOI":"10.1109\/TII.2020.3001612","article-title":"Neuromuscular Password-Based User Authentication","volume":"17","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_34","first-page":"35","article-title":"Comparative Evaluation for PCA and ICA on Tongue-Machine Interface Using Glossokinetic Potential Responses","volume":"16","author":"Gorur","year":"2020","journal-title":"Celal Bayar \u00dcniversitesi Fen Bilim. Derg."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"114961","DOI":"10.1016\/j.eswa.2021.114961","article-title":"Towards Online Applications of EEG Biometrics Using Visual Evoked Potentials","volume":"177","author":"Zhao","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"11985","DOI":"10.1109\/ACCESS.2019.2892492","article-title":"Cross-Correlation Aided Ensemble of Classifiers for BCI Oriented EEG Study","volume":"7","author":"Paranjape","year":"2019","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"13559","DOI":"10.1109\/TITS.2021.3125737","article-title":"Detection of Train Driver Fatigue and Distraction Based on Forehead EEG: A Time-Series Ensemble Learning Method","volume":"23","author":"Fan","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"145395","DOI":"10.1109\/ACCESS.2019.2939947","article-title":"A Deep Bidirectional GRU Network Model for Biometric Electrocardiogram Classification Based on Recurrent Neural Networks","volume":"7","author":"Lynn","year":"2019","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"113967","DOI":"10.1016\/j.eswa.2020.113967","article-title":"Analysis of Factors That Influence the Performance of Biometric Systems Based on EEG Signals","volume":"165","author":"Pineda","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"10901","DOI":"10.1007\/s13369-023-07798-6","article-title":"EEG-Driven Biometric Authentication for Investigation of Fourier Synchrosqueezed Transform-ICA Robust Framework","volume":"48","author":"Gorur","year":"2023","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"104996","DOI":"10.1016\/j.bspc.2023.104996","article-title":"Dyadic Boundary Points Based Empirical Wavelet Transform for the Elimination of Eye Movement and Eye Blink-Based Ocular Artifacts from EEG Signals","volume":"85","author":"Dash","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"119806","DOI":"10.1016\/j.neuroimage.2022.119806","article-title":"Comparison of Beamformer and ICA for Dynamic Connectivity Analysis: A Simultaneous MEG-SEEG Study","volume":"265","author":"Coelli","year":"2023","journal-title":"Neuroimage"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Gonsisko, C.B., Ferris, D.P., and Downey, R.J. (2023). ICanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG. Sensors, 23.","DOI":"10.3390\/s23020928"},{"key":"ref_44","unstructured":"(2023, July 07). Recurrence Plotting. Available online: https:\/\/www.mathworks.com\/matlabcentral\/fileexchange\/58246-tool-box-of-recurrence-plot-and-recurrence-quantification-analysis."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1109\/TNSRE.2014.2350537","article-title":"Frontal-Temporal Synchronization of EEG Signals Quantified by Order Patterns Cross Recurrence Analysis During Propofol Anesthesia","volume":"23","author":"Shalbaf","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"14975","DOI":"10.1007\/s00521-021-06133-0","article-title":"Improved Machine Learning Performances with Transfer Learning to Predicting Need for Hospitalization in Arboviral Infections against the Small Dataset","volume":"33","author":"Ozer","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1007\/s13246-022-01189-1","article-title":"The Single-Channel Dry Electrode SSVEP-Based Biometric Approach: Data Augmentation Techniques against Overfitting for RNN-Based Deep Models","volume":"45","author":"Gorur","year":"2022","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"13689","DOI":"10.1007\/s11042-022-13810-2","article-title":"Species-Level Microfossil Identification for Globotruncana Genus Using Hybrid Deep Learning Algorithms from the Scratch via a Low-Cost Light Microscope Imaging","volume":"82","author":"Ozer","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Eraslan, B., Gorur, K., and Temurtas, F. (2023). Novel Biometric Approach Based on Diaphragmatic Respiratory Movements Using Single-Lead EMG Signals. IETE J. Res., 1\u201322.","DOI":"10.1080\/03772063.2023.2176368"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3807","DOI":"10.1016\/j.aej.2021.02.050","article-title":"A Combined Deep Learning Application for Short Term Load Forecasting","volume":"60","author":"Ozer","year":"2021","journal-title":"Alex. Eng. J."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"119503","DOI":"10.1016\/j.eswa.2023.119503","article-title":"Detection and Classification of Pneumonia Using Novel Superior Exponential (SupEx) Activation Function in Convolutional Neural Networks","volume":"217","author":"Elen","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Haq, I., Mazhar, T., Malik, M.A., Kamal, M.M., Ullah, I., Kim, T., Hamdi, M., and Hamam, H. (2022). Lung Nodules Localization and Report Analysis from Computerized Tomography (CT) Scan Using a Novel Machine Learning Approach. Appl. Sci., 12.","DOI":"10.3390\/app122412614"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"108276","DOI":"10.1109\/ACCESS.2021.3101142","article-title":"Diabetic Retinopathy Diagnosis From Fundus Images Using Stacked Generalization of Deep Models","volume":"9","author":"Kaushik","year":"2021","journal-title":"IEEE Access"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3206","DOI":"10.1109\/TIFS.2019.2912272","article-title":"Individual Identification Based on Code-Modulated Visual-Evoked Potentials","volume":"14","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1109\/TCDS.2019.2924648","article-title":"Affective EEG-Based Person Identification Using the Deep Learning Approach","volume":"12","author":"Wilaiprasitporn","year":"2020","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.neucom.2020.06.009","article-title":"Deep Learning for EEG-Based Biometric Recognition","volume":"410","author":"Maiorana","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.inffus.2021.01.004","article-title":"A Multimodal-Siamese Neural Network (MSNN) for Person Verification Using Signatures and EEG","volume":"71","author":"Chakladar","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1038\/s41598-022-06527-7","article-title":"Towards a Universal and Privacy Preserving EEG-Based Authentication System","volume":"12","author":"Bidgoly","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Haq, I., Mazhar, T., Nasir, Q., Razzaq, S., Mohsan, S.A.H., Alsharif, M.H., Alkahtani, H.K., Aljarbouh, A., and Mostafa, S.M. (2022). Machine Vision Approach for Diagnosing Tuberculosis (TB) Based on Computerized Tomography (CT) Scan Images. Symmetry, 14.","DOI":"10.3390\/sym14101997"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Pradeep Kumar, G., Dutta, U., Sharma, K., and Ganesan, R.A. (2022, January 14\u201316). EEG-Based Biometrics: Phase-Locking Value from Gamma Band Performs Well across Heterogeneous Datasets. Proceedings of the 2022 International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany.","DOI":"10.1109\/BIOSIG55365.2022.9897042"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3626","DOI":"10.1109\/JBHI.2022.3151570","article-title":"Computerized Multidomain EEG Classification System: A New Paradigm","volume":"26","author":"Yu","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s11063-021-10546-2","article-title":"EarNet: Biometric Embeddings for End to End Person Authentication System Using Transient Evoked Otoacoustic Emission Signals","volume":"54","author":"Varugeese","year":"2022","journal-title":"Neural Process. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"11761","DOI":"10.1109\/ACCESS.2019.2963113","article-title":"Emotion Recognition From Body Movement","volume":"8","author":"Ahmed","year":"2020","journal-title":"IEEE Access"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.protcy.2013.12.338","article-title":"Classifying Electrooculogram to Detect Directional Eye Movements","volume":"10","author":"Banerjee","year":"2013","journal-title":"Procedia Technol."},{"key":"ref_65","unstructured":"(2023, July 10). Brain Mapping Plotting. Available online: https:\/\/www.mathworks.com\/matlabcentral\/fileexchange\/72729-topographic-eeg-meg-plot."},{"key":"ref_66","unstructured":"(2023, July 11). Circular Graph Plotting. Available online: https:\/\/www.mathworks.com\/matlabcentral\/fileexchange\/48576-circulargraph."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1571","DOI":"10.1109\/TMI.2019.2953584","article-title":"Imaging of Nonlinear and Dynamic Functional Brain Connectivity Based on EEG Recordings With the Application on the Diagnosis of Alzheimer\u2019s Disease","volume":"39","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1002\/hbm.25683","article-title":"Brain Functional and Effective Connectivity Based on Electroencephalography Recordings: A Review","volume":"43","author":"Cao","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"119614","DOI":"10.1016\/j.eswa.2023.119614","article-title":"Touchscreen Gestures as Images. A Transfer Learning Approach for Soft Biometric Traits Recognition","volume":"219","author":"Guarino","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"6408","DOI":"10.1109\/TII.2022.3196343","article-title":"Privacy Preserving Ear Recognition System Using Transfer Learning in Industry 4.0","volume":"19","author":"Chowdhury","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Muhammad Hussain, N., Rehman, A.U., Othman, M.T.B., Zafar, J., Zafar, H., and Hamam, H. (2022). Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data. Sensors, 22.","DOI":"10.3390\/s22145103"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Mazhar, T., Nasir, Q., Haq, I., Kamal, M.M., Ullah, I., Kim, T., Mohamed, H.G., and Alwadai, N. (2022). A Novel Expert System for the Diagnosis and Treatment of Heart Disease. Electronics, 11.","DOI":"10.3390\/electronics11233989"}],"container-title":["Biomimetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-7673\/8\/4\/378\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:37:11Z","timestamp":1760128631000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-7673\/8\/4\/378"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,18]]},"references-count":72,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["biomimetics8040378"],"URL":"https:\/\/doi.org\/10.3390\/biomimetics8040378","relation":{},"ISSN":["2313-7673"],"issn-type":[{"value":"2313-7673","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,18]]}}}